Chapter 4 Data processing
The corresponding XMAS2 argument is normMethod with the available options: “rarefy”, “TSS”, “TMM”, “RLE”, “CSS”, “CLR” and “CPM”.
| Method | Approach | Comments |
|---|---|---|
| rarefy (Rarefying) | Subsampling from the data to obtain samples with equal library size (also called rarefaction level) . | Aims to avoid compositional effects. |
| TSS (Total Sum Scaling) | Traditional approach for building fractions. Counts are divided by the total sum of counts in the corresponding sample. | Strongly influenced by highly abundant taxa. |
| TMM (Trimmed Mean of M-values) | First, a sample is chosen as reference. The scaling factor is then derived using a weighted trimmed mean over the differences of the log-transformed gene-count fold-change between the sample and the reference. | Aims to avoid sequencing depth effects. |
| RLE (Relative Log Expression ) | RLE uses a pseudo-reference calculated using the geometric mean of the gene-specific abundances over all samples. The scaling factors are then calculated as the median of the gene counts ratios between the samples and the reference. | Similar to TMM, this normalization method is based on the hypothesis that the most genes are not DE. |
| CSS (Cumulative Sum Scaling) | Within each sample, the counts are summed up to a predefined quantile. The counts are then divided by this sum. | Aims at avoiding the influence of highly abundant taxa. |
| CLR (Centered log-ratio transformation) | For a composition \[x = (x_{1} , ..., x_{p})\] the clr transformation is defined as \[clr = log(\frac{x_{1}}{g(x)}, ..., \frac{x_{p}}{g(x)})\], where \[g(x)=(\prod_{p}^{k=1} x_{k})^{\frac{1}{p}}\] is the geometric mean. | Aims to avoid compositional effects. |
| CPM (Counts Per Million) | pre-sample normalization of the sum of the values to 1e+06. | Aims to avoid sequencing depth effects. |
Loading packages
This part has too may procedures and we only choose some of them. Please go to XMAS tutorial: Chapter 6 to see more approaches and details for being familiar with this part.
4.1 Extracting specific taxonomic level
- Removing spike-in sample (BRS)
metaphlan2_ps_remove_BRS <- get_GroupPhyloseq(
ps = metaphlan2_ps,
group = "Group",
group_names = "QC",
discard = TRUE)
metaphlan2_ps_LOD_species_remove_BRS## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 192 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 192 taxa by 7 taxonomic ranks ]- Species
metaphlan2_ps_LOD_species <- aggregate_LOD_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Species",
cutoff = 1e-04)
metaphlan2_ps_LOD_species## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 190 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 190 taxa by 7 taxonomic ranks ]- Genus
metaphlan2_ps_LOD_genus <- aggregate_LOD_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Genus",
cutoff = 1e-04)
metaphlan2_ps_LOD_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 66 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 66 taxa by 6 taxonomic ranks ]- Phylum
metaphlan2_ps_LOD_phylum <- aggregate_LOD_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Phylum",
cutoff = 1e-04)
metaphlan2_ps_LOD_phylum## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 7 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 7 taxa by 2 taxonomic ranks ]Finally, we obtained the final phyloseq-class object.
4.2 Systematic Information
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-10-27
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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## systemfonts 1.0.4 2022-02-11 [2] CRAN (R 4.1.2)
## tensorA 0.36.2 2020-11-19 [2] CRAN (R 4.1.0)
## TH.data 1.1-2 2023-04-17 [2] CRAN (R 4.1.2)
## tibble * 3.2.1 2023-03-20 [2] CRAN (R 4.1.2)
## tidyr * 1.3.0 2023-01-24 [2] CRAN (R 4.1.2)
## tidyselect 1.2.0 2022-10-10 [2] CRAN (R 4.1.2)
## timechange 0.2.0 2023-01-11 [2] CRAN (R 4.1.2)
## truncnorm 1.0-9 2023-03-20 [2] CRAN (R 4.1.2)
## tzdb 0.4.0 2023-05-12 [2] CRAN (R 4.1.3)
## umap 0.2.10.0 2023-02-01 [2] CRAN (R 4.1.2)
## units 0.8-2 2023-04-27 [2] CRAN (R 4.1.2)
## urlchecker 1.0.1 2021-11-30 [2] CRAN (R 4.1.0)
## usethis * 2.2.2 2023-07-06 [2] CRAN (R 4.1.3)
## utf8 1.2.3 2023-01-31 [2] CRAN (R 4.1.2)
## uuid 1.1-0 2022-04-19 [2] CRAN (R 4.1.2)
## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.1.3)
## vegan * 2.6-4 2022-10-11 [2] CRAN (R 4.1.2)
## VennDiagram * 1.7.3 2022-04-12 [2] CRAN (R 4.1.2)
## VGAM 1.1-8 2023-03-09 [2] CRAN (R 4.1.2)
## viridis * 0.6.3 2023-05-03 [2] CRAN (R 4.1.2)
## viridisLite * 0.4.2 2023-05-02 [2] CRAN (R 4.1.2)
## vroom 1.6.3 2023-04-28 [2] CRAN (R 4.1.2)
## webshot 0.5.5 2023-06-26 [2] CRAN (R 4.1.3)
## WGCNA 1.72-1 2023-01-18 [2] CRAN (R 4.1.2)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.1.2)
## Wrench 1.12.0 2021-10-26 [2] Bioconductor
## xfun 0.40 2023-08-09 [1] CRAN (R 4.1.3)
## XMAS2 * 2.2.0 2023-10-27 [1] local
## XML 3.99-0.14 2023-03-19 [2] CRAN (R 4.1.2)
## xml2 1.3.5 2023-07-06 [2] CRAN (R 4.1.3)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.1.0)
## XVector 0.34.0 2021-10-26 [2] Bioconductor
## yaml 2.3.7 2023-01-23 [2] CRAN (R 4.1.2)
## zCompositions 1.4.0-1 2022-03-26 [2] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [2] Bioconductor
## zoo 1.8-12 2023-04-13 [2] CRAN (R 4.1.2)
##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
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